Braking systems and methods for determining dynamic braking data for a braking model for a train

ABSTRACT

Disclosed is a computer-implemented method for determining dynamic braking data for use in a braking model of at least one train, the method including: (a) determining at least one initial safety factor; (b) determining at least one dynamic braking adjustment factor based at least partially on (i) the expected dynamic braking force, and (ii) specified retarding forces of the train; and (c) determining at least one new safety factor based at least partially on the at least one initial safety factor and the at least one dynamic braking adjustment factor. Also disclosed are braking systems including dynamic braking for a train having at least one locomotive.

CROSS-REFERENCE TO RELATED APPLICATIONS

This disclosure claims the benefit of U.S. Provisional Application No.61/824,569, filed May 17, 2013, which is hereby incorporated byreference in its entirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to train control and brakingsystems, and in particular to braking systems and methods of determiningdynamic braking data and information for use in a braking model oralgorithm on an operating train.

2. Description of the Related Art

As is known in the art, trains, which include at least one locomotiveand, typically, multiple railcars, employ complex braking systems andarrangements for slowing or stopping the train in variety of conditionsand environments. For example, existing braking systems are shown anddescribed in U.S. Publication No. 2007/0142984 and U.S. Pat. Nos.8,019,496; 6,314,358; 5,744,707; 4,562,543; 4,384,695; 4,235,402;4,005,838; 4,005,837; 3,921,946; and 3,731,193. Further, many trainsystems and networks use some form of computer-controlled trainmanagement system, such as a Positive Train Control (PTC) system (e.g.,the I-ETMS® of Wabtec Corporation). These computer-controlled trainmanagement systems have on-board computers or controllers that are usedto implement certain train control and management actions for ensuringsafe and effective operation of the train.

In addition, the computerized braking control system of the trainmanagement system uses a braking model or algorithm to build ordetermine stopping distances as the train advances or travels throughthe train network. Such stopping distances are based upon certainspecified train-based operating parameters and/or variable feedback froma number of sensor systems and/or ancillary measurements ordeterminations, e.g., track grade, track curvature, train speed, trainweight, brake pipe pressure, braking system reservoir pressures, and thelike. Accordingly, the braking model must account for those variousparameters, but must also account for variation in the system parameterswhile providing a stopping distance that has a very low probability ofstopping the train past the target location.

As is also known, these stopping distances are used to build a brakingprofile or curve that estimates or predicts when train will stop, suchas at a specified target point or area that is positioned ahead on thetrack. This braking profile is continually calculated using the brakingmodel and using the changing feedback and variable determinations toprovide an updated braking profile or curve ahead of the train. Ingeneral, this braking profile or curve visually illustrates to the trainoperator where the train is predicted to stop if a full-service penaltybrake application is initiated. Again, this braking profile or curve iscontinually (e.g., 1-3 times per second) updated so that the operatorhas an ongoing understanding of how and when the train would stop duringa penalty brake situation.

The braking model or algorithm is initially developed by executing amultitude of scenarios under a wide variety of conditions and statesrelated to all aspects of the train and its projected surroundingenvironment. Further, and based upon certain rules and/or standards, asafety factor is determined to ensure to a specified probability thatthe required stopping distance will be safely short of the target. Stillfurther, and during a penalty brake application, the braking modelcontinues to monitor and predict the stopping distance to the specifiedtarget location. While a prediction that the train will stop before orat the target location may not pose a significant safety issue, apredicted stop after the target location could prove problematic orunsafe.

In order to provide additional braking capacity and functionality, manytrains are equipped with a Dynamic Brake System, which uses the tractionmotors of a railroad vehicle as generators during the braking process.Specifically, such a Dynamic Brake System provides additional brakingforce for the train by turning the motors that drive the wheels intogenerators and transferring the energy into resistors. In the past, andas discussed, the PTC braking model or algorithm has been developed tosafely predict the stopping distance and characteristics of a train sothat the PTC system can prevent the train from exceeding any speedrestrictions or authority limitations. Through years of development,this braking model or algorithm has been refined to achieve accurateresults within the requirements for safe operation.

However, one force that has never been properly accounted for is thetotal dynamic braking forces produced by the locomotive consist. Thedynamic braking force has been excluded primarily based on guidance fromthe Federal Railroad Administration (FRA) and their belief that it couldnot be safely accounted for or relied upon. The drawback for therailroad operators is that by excluding dynamic braking force, the PTCsystem becomes too conservative, and may slow down overall throughput onthe railroad due to excessive warnings and/or unnecessary enforcements.Therefore, accounting for dynamic braking force in the PTC braking modelor algorithm has the potential to improve rail network throughput andreduce nuisance warning and enforcement events to crews that areproperly handling their train.

SUMMARY OF THE INVENTION

Generally, provided are braking systems and methods for determining orderiving accurate dynamic braking data for a braking model for a trainthat address and/or overcome some or all of the above-identifieddeficiencies and drawbacks associated with existing train brakingsystems and computer-controlled train management systems. Preferably,provided are braking systems and methods for determining or derivingaccurate dynamic braking data for a braking model for a train thatprovide a more accurate braking model or algorithm for use in a trainmanagement system. Preferably, provided are braking systems and methodsfor determining or deriving accurate dynamic braking data for a brakingmodel for a train that lead to a more productive train management systemand improved railroad throughput.

Accordingly, and in one preferred and non-limiting embodiment, providedis a computer-implemented method for determining dynamic braking datafor use in a braking model of at least one train. The method includes:(a) determining at least one initial safety factor; (b) determining atleast one dynamic braking adjustment factor based at least partially on(i) the expected dynamic braking force, and (ii) specified retardingforces of the train; and (c) determining at least one new safety factorbased at least partially on the initial safety factor and the dynamicbraking adjustment factor.

In another preferred and non-limiting embodiment, provided is a brakingsystem including dynamic braking for a train having at least onelocomotive with at least one on-board computer configured or programmedto: (a) before or during at least one braking event, determine, sense,and/or measure the operating status, performance, available force,and/or condition of at least one of the following: (i) at least onelocomotive; (ii) at least one locomotive consist; (iii) at least onecomponent of a dynamic brake system, or any combination thereof; and (b)adjust at least one variable of the on-board braking model based atleast partially on the determined, sensed, and/or measured operatingstatus, performance, available force, and/or condition.

In a further preferred and non-limiting embodiment, provided is abraking system including dynamic braking for a train having at least onelocomotive with at least one on-board computer configured or programmedto: (a) before or during at least one braking event, determine predictedacceleration or deceleration of the train based at least partially uponan on-board braking model; (b) during the at least one braking event,determine actual train acceleration or deceleration of the train basedat least partially upon sensed, measured, and/or calculated operatingconditions; and (c) adjust at least one variable of the on-board brakingmodel based at least partially on a specified difference between thepredicted acceleration or deceleration and the actual acceleration ofdeceleration.

These and other features and characteristics of the present invention,as well as the methods of operation and functions of the relatedelements of structures and the combination of parts and economies ofmanufacture, will become more apparent upon consideration of thefollowing description and the appended claims with reference to theaccompanying drawings, all of which form a part of this specification,wherein like reference numerals designate corresponding parts in thevarious figures. It is to be expressly understood, however, that thedrawings are for the purpose of illustration and description only andare not intended as a definition of the limits of the invention. As usedin the specification and the claims, the singular form of “a”, “an”, and“the” include plural referents unless the context clearly dictatesotherwise.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart of one embodiment of a train braking system andmethod according to the principles of the present invention;

FIG. 2 is a schematic diagram of one embodiment of a train control andbraking system according to the principles of the present invention; and

FIG. 3 is a schematic diagram of a computer and network infrastructureaccording to the prior art.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

For purposes of the description hereinafter, the terms “end”, “upper”,“lower”, “right”, “left”, “vertical”, “horizontal”, “top”, “bottom”,“lateral”, “longitudinal” and derivatives thereof shall relate to theinvention as it is oriented in the drawing figures. However, it is to beunderstood that the invention may assume various alternative variationsand step sequences, except where expressly specified to the contrary. Itis also to be understood that the specific devices and processesillustrated in the attached drawings, and described in the followingspecification, are simply exemplary embodiments of the invention. Hence,specific dimensions and other physical characteristics related to theembodiments disclosed herein are not to be considered as limiting.

As used herein, the terms “communication” and “communicate” refer to thereceipt, transmission, or transfer of one or more signals, messages,commands, or other type of data. For one unit or device to be incommunication with another unit or device means that the one unit ordevice is able to receive data from and/or transmit data to the otherunit or device. A communication may use a direct or indirect connection,and may be wired and/or wireless in nature. Additionally, two units ordevices may be in communication with each other even though the datatransmitted may be modified, processed, routed, etc., between the firstand second unit or device. For example, a first unit may be incommunication with a second unit even though the first unit passivelyreceives data, and does not actively transmit data to the second unit.As another example, a first unit may be in communication with a secondunit if an intermediary unit processes data from one unit and transmitsprocessed data to the second unit. It will be appreciated that numerousother arrangements are possible. Any known electronic communicationprotocols and/or algorithms may be used such as, for example, TCP/IP(including HTTP and other protocols), WLAN (including 802.11 and otherradio frequency-based protocols and methods), analog transmissions,and/or the like. Further, a variety of wired or wireless network devicesmay be used, including, but not limited to, a wireless network device, awired network device, a WiFi network device, a Bluetooth network device,a Zigbee network device, a WirelessHART network device, a GPRS networkdevice, an ultra-wideband network device, a cable network device, awide-band network device, a multi-radio network device, and the like.

As discussed, a primary issue with utilizing dynamic braking is that theforce is not guaranteed to be present throughout a braking event. Thisforce can be limited or non-existent due to mechanical failure, orintentional or unintentional cut-out. Unlike an air brake penalty, thecrew could potentially manipulate the dynamic braking force during apenalty stop. Since it is a retarding force, if the braking model oralgorithm includes the force, and it is not present or reduced in anyway, the braking model or algorithm may not be accurate, which leads toa higher potential to allow a target over-speed or overrun, whichnegatively impacts the safe performance of the system. Accordingly, thepresent invention is directed to braking systems and methods ofdetermining dynamic braking data for a braking model for a train, asshown in certain preferred and non-limiting embodiments, and inflowchart and schematic form, in FIGS. 1 and 2.

Dynamic braking force is generated by a dynamic re-wiring of thelocomotive traction motors into generators. In such a configuration, thegenerator would spin freely until connected to a resistive load. When agenerator is connected to a load it requires a mechanical force to spinthe generator, and this mechanical force is supplied by the movinglocomotive. Accordingly, the speed of the locomotive is retarded by thisgenerated force, and the resulting energy is turned into heat in theresistive load. Existing dynamic braking systems and arrangementsexhibit several practical and implementation constraints. First, thelocomotive must be moving at a minimum speed. Above some maximum speed,there is no practical method of absorbing the energy. For example, aparticular locomotive may generate zero dynamic brake force below 3 mph.Between 3 and 10 mph, it might produce from 0 to 10,000 pounds of forceper traction motor, based on a linear interpolation. Between 10 and 30mph the locomotive can produce a relatively constant 10,000 pounds offorce per traction motor. From 30 to 40 mph, the force may again belinearly de-rated to 0 pounds. These speed ranges and forces vary bylocomotive model. As is known, the engineer has a control that can varythe application ratio of dynamic brakes from 0 to 100%. The “expecteddynamic braking force” is therefore a function of the locomotive type,the number of locomotives, the number of traction motors, the currentspeed, and the current setting of the dynamic brake control handle bythe engineer.

As used hereinafter, various terms may be defined or expressed asfollows, without limitation. “Total Retarding Forces” may include: (1)Grade Force—The force of gravity acting on the mass of the train goingup a hill; (2) Curvature Force—Side wheel friction as the train goesthrough curves; (3) Aerodynamic Force—The shape and contour of cars andlocomotives produce a force as a function of speed; (4) FrictionForce—The mechanical friction of the cars and wheel bearings; and (5)Dynamic Brake Force—The forces generated by dynamic brakes.

“Total Axle Count” refers to the sum of all the axles that could producedynamic brake force. For the purpose of the present invention, and inone preferred and non-limiting embodiment, the total axle count is theproduct of the number of “Cut-In Locomotives” times the “Axles perLocomotive”. In another preferred and non-limiting embodiment, the“Total Axle Count” may be a sum of products. For example, two six-axlelocomotives and two four-axle locomotives would be (2*6)+(2*4)=20.

“Cut-in locomotive” refers to a locomotive where the operationalcontrols of the locomotive are set to produce dynamic brake force whenrequested. The opposite state is a “Cut-out Locomotive,” which mayphysically be in the train, but where electrical problems, mechanicalproblems, operational policy, and/or railroad rules may result in anoperational state for that locomotive to be such that it will be knownthat it cannot produce a dynamic braking force. “Axle per Locomotive”refers to the axle count on a per-locomotive basis. Most locomotiveshave one traction motor per axle (although other configurations arepossible). Some locomotive manufacturers allow the cut-out of individualtraction motors. Other locomotive manufacturers allow the cut-out of allthe traction motors on a bogie. As is known, all rail vehicles arelimited to 70,000 pounds of weight per axle. If this limit wereexceeded, the rail may be crushed. Therefore, if a locomotive weighs 207tons (414,000 pounds), it must have 414,000/70,000=5.91 axles (6 axles).This number of axles will determine the number of traction motors, andthe resulting possibility of producing a dynamic brake force.

“Dynamic Brake Axle Count” refers to the number of axles of dynamicbrakes being used in a particular calculation (in time) of dynamicbraking effort (or dynamic braking force). The results of thecalculation of the acceleration may result in the “Dynamic Brake AxleCount” remaining the same, increasing, or decreasing, as describedotherwise herein. “Max Dynamic Axle Count Per Rule” refers to themaximum allowable dynamic axle count. In particular, too much dynamicbraking force in a locomotive consist can cause excessive or unsafein-train car forces to develop. Some newer locomotives can produce moredynamic brake force than their physical axle count would indicate. Whenassigning a locomotive power consist to a train, a railroad typicallytakes certain factors into account. Depending on the railroads ratingsystem, a range of from 24 axles to 28 axles is included in a rule forassigning locomotives to a train. For normal extended range dynamicbrakes and a railroad with a 24-axle maximum, six four-axle locomotivesor 4 six-axle locomotives (or other combinations) would be considered asthe maximum allowed number of locomotives to be in the locomotiveconsist with dynamic brake axles cut-in. New locomotives with six axlesof highly effective dynamic brakes can be rated as if they had twelveequivalent axles of normal dynamic brake effort. In this case, two suchlocomotives would be allowed to have all their dynamic brakes cut in tocomply with a 24-axle maximum rule.

“De-rated Axle Count” refers to the dynamic axles that are excluded fromthe algorithm or determination, as discussed hereinafter. When theacceleration calculation leads to the determination or prediction thattoo much dynamic brake force is or will be present, one or more axles ofdynamic brake is excluded from the determination, and added to thede-rated axle count. Further determinations may maintain, add to, orsubtract from the de-rated axle count. “Dynamic Braking Force per Axle”is the expected or determined dynamic braking force on a per-axle basis.The individual traction motor on each axle is the lowest unit of measurefor which a dynamic braking force is either available or not available.At this level, the dynamic brake force generated is a function of theoriginal design and speed of the train, i.e., the expected dynamicbraking force. At 100% actuation, and in the most effective speed range,10,000 pounds of dynamic braking force is typical.

“Dynamic Braking Excitation Measurements” refers to the determinationsor measurements that relate to dynamic braking excitation. Alllocomotives in a consist must be engaged in the same operation, ascontrolled by the locomotive engineer. To facilitate this, and as isknown, there is a 27-pin Multiple Unit cable that connects onelocomotive to the next. This interconnecting cable and the wiring oneach locomotive are collectively called the Train Line (TL). In oneembodiment, Train Line 21 controls dynamic brake excitation. The voltageon the TL ranges from 0 to 74 volts. Whatever this voltage is, all ofthe locomotives respond in a like manner. If the voltage is 0 volts, theengineer's control lever is set to request 0% of the available dynamicbrake force. If the voltage is set to 74 volts, the engineer's controllever is set to request 100% of the available dynamic brake force. Thevoltage on TL 21 is continually sensed to determine the expectedpercentage of dynamic brake force to use based on the current speed.

The present invention is directed to braking systems and methods fordetermining dynamic braking data for a braking model for a train. Thesystems and methods described herein represent computer-implementedsystems and methods, and may also be referred to as a model, algorithm,process, method, or the like. Accordingly, and as discussed hereinafter,the computers, servers, and devices represent specially-programmedcomputers having program instructions adapted to, configured to,programmable to implement, or capable of implementing, the describedmethods and processes. For example, some or all of the systems ormethods described herein may be wholly or partially implemented on orexecuted by a train management computer of a train, an on-board computerof a train, a remote server, a back office system, or the like.

In a first primary preferred and non-limiting embodiment, the presentinvention includes a system and method that allows for the safe use ofdynamic braking in the braking model or algorithm, and reduction ofinitial errors in dynamic braking force, by adjusting the safety factor(or offset) in the brake model or algorithm in accordance to the amountof dynamic braking expected. In a second primary preferred andnon-limiting embodiment, the present invention includes a system andmethod that allows for the safe use of dynamic braking in the brakingmodel or algorithm, and reduces the effect of any possible failure ofthis force, by performing a real-time monitoring of the train behaviorto “learn” and adjust the calculated dynamic braking force to anaccurate level. This also ensures that safety considerations are beingmet.

With respect to the first primary preferred and non-limiting embodiment,and since the braking curve is always a calculation into or predictionof the future, the on-board computer or computer, e.g., the trainmanagement computer, on-board computer, and the like, cannot determinewhat might happen to the dynamic braking forces in the future. Toaccount for this, the safety factor can be adjusted to compensate forthe risk that the dynamic braking might not be available. In onepreferred and non-limiting embodiment, the method includes: (a)determining at least one initial safety factor; (b) determining at leastone dynamic braking adjustment factor based at least partially on (i)the expected dynamic braking force, and (ii) specified retarding forcesof the train; and (c) determining at least one new safety factor basedat least partially on the initial safety factor and the dynamic brakingadjustment factor. In another preferred and non-limiting embodiment, thesystem generates or modifies the braking model or algorithm byincorporating or using the at least one new safety factor, and thisbraking model or algorithm can be provided to at least one on-boardcomputer of the train. In addition, some or all of the above-discussedsteps can be implemented or performed on or by an on-board computer ofthe train.

In another preferred and non-limiting embodiment, the process, method,or algorithm adjusts the safety factor (or offset) by the ratio of theexpected dynamic braking force compared to other retarding forces on thetrain. The greater the expected dynamic braking force, the greater thesafety factor (or offset) that is added. In this embodiment, the initialprocess, method, or algorithm uses a straight ratio as follows: newsafety factor=initial safety factor*(1+(dynamic braking force/totalretarding forces). It is envisioned that this process, method, oralgorithm or calculation may be modified or otherwise refined within thespirit and scope of the present invention. Although the safety factor isbeing increased with dynamic brakes present, the overall predictedstopping distance decreases as dynamic brake force is accounted for inthe braking model or algorithm.

In the second primary preferred and non-limiting embodiment, and eventhough the braking curve generated by the braking model or algorithm ismainly a future prediction, it can be made much more accurate byapplying real-time behavior measurement to make adjustments to theexpected future behavior. As is known, the current brake model oralgorithm is based on Newton's first law of motion: F=m*a or a=F/m. Thebraking model or algorithm is continually computing the expectedacceleration or deceleration of the train. If the real-time accelerationor deceleration of the train is monitored and compared against thepredicted acceleration or deceleration of the train, then any mechanicalfailure or cut-out of dynamic brakes can be accounted for. Theseadjustments can quickly propagate into the future modeling, and thus notbe a significant safety risk for operating the train.

Accordingly, in one preferred and non-limiting embodiment, provided is abraking system including dynamic braking for a train having at least onelocomotive with at least one on-board computer configured or programmedto: (a) before or during at least one braking event, determine predictedtrain acceleration or deceleration of the train based at least partiallyupon an on-board braking model; (b) during the at least one brakingevent, determine actual train acceleration or deceleration of the trainbased at least partially upon sensed, measured, and/or calculatedoperating conditions; and (c) adjust at least one variable of theon-board braking model based at least partially on a specifieddifference between the predicted train acceleration or deceleration andthe actual train acceleration of deceleration. In one preferred andnon-limiting embodiment, at least one of steps (a)-(c) is implemented oroccurs substantially in real-time. In one embodiment, the variableincludes or is in the form of dynamic braking force data.

In another preferred and non-limiting embodiment, the on-board brakingmodel or algorithm is generated based at least partially on a determinedretarding force provided by each equipped or applicable axle of thetrain, and the retarding force is based at least partially on the levelof dynamic brake excitation and/or measured dynamic brake energy. Inthis embodiment, the retarding force is determined based at leastpartially on determining, sensing, and/or measuring the operatingstatus, performance, available force, and/or condition of at least oneof the following: (i) the at least one locomotive; (ii) at least onelocomotive consist; (iii) at least one component of a dynamic brakesystem, or any combination thereof. In addition, the determination ofthe retarding force can be based at least partially on railroadoperating rules and/or cut-out axles.

As discussed, the dynamic braking portion of the braking model oralgorithm is based on a retarding force provided by each axle in thelocomotive consist. The computed force is based on the level of dynamicbraking excitation and/or the dynamic braking energy being measured. Italso provides for a maximum number of dynamic braking axles, asdetermined by railroad operating rules. It further accounts for anyknown axles that are cut-out, based on consist information. Accordingly,in one preferred and non-limiting embodiment, the dynamicbrake-generated retarding force is determined using the followingformulae:total axle count=(number of cut-in locomotives)*(axles per locomotive)if (total axle count>max DB axle count per rule) then (total axlecount=max axle count)retarding force=(total axle count−de-rated axle count)*(DB force peraxle).

In this embodiment, the dynamic braking portion of the braking model oralgorithm initially assumes that the remaining axles are providingdynamic braking force in accordance with dynamic braking excitationmeasurements. The actual and predicted acceleration or deceleration isthen accumulated over a time period (e.g., about 10 seconds), and,optionally, normalized. These normalized readings are then compared. Inone preferred and non-limiting embodiment, if the actual acceleration isgreater (e.g., 0.5 ft/sec/sec) than the predicted acceleration, one“axle's worth” of force is removed or de-rated for future calculationsin the braking model or algorithm. This immediately affects the brakingdistance by making it slightly or incrementally longer and safer. Inthis embodiment, the dynamic braking portion of the braking model oralgorithm is reset and another average is computed. Again, if the actualacceleration is greater than the predicted acceleration, another axle isremoved or de-rated. This process continues or repeats until thepredicted and actual accelerations are balanced, or all dynamic brakingaxles have been removed or de-rated.

In another preferred and non-limiting embodiment, the dynamic brakingportion of the braking model or process, method, or algorithm initiallyassumes a minimal number of axles are providing dynamic braking force inaccordance with dynamic braking excitation measurements. The actual andpredicted acceleration or deceleration is then accumulated over a timeperiod (e.g., about 10 seconds), and, optionally, normalized. Thesenormalized readings are then compared. In one preferred and non-limitingembodiment, if the actual acceleration is less (e.g., 0.5 ft/sec/sec)than the predicted acceleration, one axle's worth of force is added forfuture calculations in the braking model or algorithm. This provides fora conservative and safe initial estimate of dynamic braking capability,and then reduces this “conservativeness” by making the predictedstopping distance incrementally shorter, as validated by acceleration.In this embodiment, the dynamic braking portion of the braking model oralgorithm is reset and another average is computed. Again, if the actualacceleration is less than the predicted acceleration, another axle isadded. This process continues or repeats until either the maximum numberof available axles has been reached, or some specified (or conservative)limit below that number has been reached.

In another preferred and non-limiting embodiment, thecomputer-implemented method or process includes: (a) if the actual traindeceleration is less than the predicted train deceleration by aspecified amount, the adjustment step (c) comprises: (i) removing oneaxle's worth of force; or (ii) de-rating one axle's worth of force, insubsequent brake model calculations; or (b) if the actual traindeceleration is greater than the predicted train deceleration by aspecified amount, the adjustment step (c) comprises at least one of: (i)adding one axle's worth of force; or (ii) rating one axle's worth offorce, in subsequent brake model calculations. In addition, step (c) isrepeated for the predicted train acceleration or deceleration and theactual train acceleration or deceleration over a subsequent period oftime. Upon reducing the difference between the predicted trainacceleration or deceleration and the actual train acceleration ordeceleration to a specified level, the method and process of thisembodiment adjusts the braking model for subsequent braking events. Inanother preferred and non-limiting embodiment, the above-discussedsafety factor is generated by: (a) receiving or determining at least oneinitial safety factor; (b) receiving or determining at least one dynamicbraking adjustment factor based at least partially on (i) the expecteddynamic braking force, and (ii) specified retarding forces of the train;and (c) determining at least one new safety factor based at leastpartially on the initial safety factor and the dynamic brakingadjustment factor.

In a still further preferred and non-limiting embodiment, provided is abraking system including dynamic braking for a train having at least onelocomotive with at least one on-board computer configured or programmedto: (a) before or during at least one braking event, determine, sense,and/or measure the operating status, performance, available force,and/or condition of at least one of the following: (i) at least onelocomotive; (ii) at least one locomotive consist; (iii) at least onecomponent of a dynamic brake system, or any combination thereof; and (b)adjust at least one variable of the on-board braking model based atleast partially on the determined, sensed, and/or measured operatingstatus, performance, available force, and/or condition. Accordingly, thesystem could also make use of other systems on the locomotive thatreport dynamic brake health and available force in the lead locomotiveand trailing locomotives in the consist. This may be implemented using adynamic brake monitor system, and it provides a basis or platform forcommunication between locomotives so the engineer can see the dynamicbrake system status of the whole locomotive consist.

In another preferred and non-limiting embodiment, (a) if the actualtrain deceleration is less than the predicted train deceleration by aspecified amount, the adjustment step (b) comprises: (i) removing oneaxle's worth of force; or (ii) de-rating one axle's worth of force, insubsequent brake model calculations; or (b) if the actual traindeceleration is greater than the predicted train deceleration by aspecified amount, the adjustment step (b) comprises at least one of: (i)adding one axle's worth of force; or (ii) rating one axle's worth offorce, in subsequent brake model calculations. This calculation processis then repeated for another period of time, and the process continuesrepeating until the predicted and actual decelerations are balanced orall dynamic braking axles have been removed or added, or de-rated orrated.

Using these iterative processes, the braking model or algorithm “learns”the actual amount of dynamic braking force on a specified locomotiveand/or consist. This learned data and information can now be applied toall future stops involving dynamic braking. Further, this dynamicbraking retarding force data can be reset or erased when appropriate,such as when new consist information is provided, or the system has beenre-initialized. As discussed the dynamic braking portion of the brakingmodel or algorithm can easily be modified to start with a lesser assumedforce, and then add or subtract axles. Further, a safety analysis willhelp determine the proper approach. It should be noted that thesemethods and systems may also account for any “phantom” force that may bealtering the acceleration or deceleration of the train, even though itis assumed to be a dynamic braking error. Further the variables andconstants in the above formulae may be modified or revised withoutdeparting from the spirit and scope of the present invention.

In another preferred and non-limiting embodiment, the first and secondprimary embodiments are combined, and work in conjunction with eachother to minimize any “surprises” to the crew. Since the safety factoris based on the amount of expected dynamic braking, as the braking modelor algorithm “learns” reduced or varied levels of dynamic braking, theeffect is to lengthen the braking curve, but with a decrease in thesafety factor (or offset). Thus, the stopping distance presented to thecrew is only gradually changing, therefore avoiding sudden or surprisewarnings and enforcements. By implementing one or both of theseapproaches, a significant reduction is provided in the predictedstopping distance of the train on steep downhill grades, where dynamicbraking is heavily used. Further, by implementing one or both of theseapproaches, alignment and/or convergence are improved between thepredicted train behavior and actual train behavior. This will improvecrew confidence in the system, and improve the railroads overallthroughput by avoiding unnecessary enforcements in scenarios where thecrew is properly controlling the train.

One preferred and non-limiting embodiment of the present invention isillustrated in FIG. 1. In this embodiment, the system and method of thepresent invention is implemented for a braking algorithm that includesdynamic braking, and utilizes the real-time train behavior to adjust itscalculations. In this embodiment, the safety factor is adjusted based onthe dynamic braking level. With reference to FIG. 1, the process beginswith a Determination of the Maximum DB Axle Count (Step 100), whichdetermines the maximum available dynamic braking axle count. The TotalDB Axles Possible (Step 102) is determined by checking the Number ofCut-in Locomotives, the number of Axles Per Locomotive, as limited byany Maximum DB Axles Per Train limit to the calculation. The Total DBAxles Possible is then provided to a process that limits the total axlesby a De-rated Axle Count and then Computes a DB Force Per Axle (Step104), which is the dynamic braking force per axle that would begenerated by each remaining axle, and sums those dynamic braking forces.The result is the Expected DB Force being generated by the train.

Next, the process Predicts Deceleration (Step 106) beginning with thesummation of forces, which sums the Expected DB Force and all of theother forces that have been computed and/or are acting upon the train,including, but not limited to Grade Force, Curvature Force, Air BrakeForce, and Resistive Forces. Using the summed force and the Train Mass,a Predicted Deceleration is computed. Additionally, during theprediction process, a DB Ratio of the amount of dynamic braking forceversus other forces acting on the train is computed for use indetermining an appropriate safety factor for the braking distance. Afteriterating this calculation over time, a Predicted Stopping Distance isdetermined (as discussed hereinafter).

Next, in Step 108, the Predicted Deceleration is compared with theActual Deceleration measured for the train. A determination is manuallyor automatically made (such as through the use of a configurable rangeor margin) as to how close the Predicted Deceleration and ActualDeceleration are. If the Predicted Deceleration and the ActualDeceleration do not match closely or within the set range or margin,then the De-rated Axle Count is adjusted up or down accordingly (at Step110). If the Predicted Deceleration is greater than the ActualDeceleration, the De-rated Axle Count is increased by one. If thePredicted Deceleration is less than the Actual Deceleration, theDe-rated Axle Count is decreased by one. The new De-rated Axle Count isthen fed back for the next iteration of the process, method, oralgorithm thereby increasing or decreasing the amount of expecteddynamic braking force. If the actual and predicted decelerations match(i.e. are within the range or margin), then the process, method, oralgorithm will Maintain the DB Axle Count (Step 112). In parallel withthe Predict Deceleration process, the DB Ratio is used to Adjust theSafety Factor for Using DB (Step 114), which will affect the brakingdistance of the train. The Computed Safety Factor From the Air BrakeModel (Step 116) is increased proportionally to the amount of dynamicbraking force used in the braking distance calculations. This adjustmentis then applied to the Predicted Stopping Distance Calculation (Step118). It should be noted that the Predicted Deceleration and ActualDeceleration components or variables may be changed to PredictedAcceleration and Actual Acceleration, and the methodology adjustedaccordingly.

The presently-invented system and methods can be implemented inconnection with a variety of train types and railroad systems. In onepreferred and non-limiting embodiment, and as illustrated in FIG. 2, thesystems and methods described herein may be implemented on a train withat least one locomotive 10 having an on-board computer system 12 (e.g.,an on-board controller, an on-board computer, a train managementcomputer, and the like). The on-board computer system 12 includes adatabase 14 populated with track profile data 16 and train data 18, andthe on-board computer system 12 also includes the appropriate brakingmodel and other software or programs to effectively implement thesystems and methods according to the present invention. In thisembodiment, the on-board computer system 12 receives real-time inputsfrom various locomotive control settings 20, dynamic brake settings 21,a GPS receiver 22, and/or at least one speed sensor 24. The on-boardcomputer system 12 is in communication with, integrated with, orcontrols the braking system 26, which includes a penalty brake actuator28 and an emergency brake actuator 30. Accordingly, thepresently-invented system and methods can be effectively implemented andused by or on such a locomotive 10 having such an on-board computersystem 12 and braking system 26, including a dynamic brake system ordynamic braking functionality. It is envisioned that any type of trainmanagement system (or Positive Train Control (PTC) system) and brakingsystem and arrangement can be used within the context and scope of thepresent invention.

The present invention, including the various computer-implemented and/orcomputer-designed aspects and configurations, may be implemented on avariety of computing devices and systems, including the client devicesand/or server computer, wherein these computing devices include theappropriate processing mechanisms and computer-readable media forstoring and executing computer-readable instructions, such asprogramming instructions, code, and the like. In addition, aspects ofthis invention may be implemented on existing controllers, controlsystems, and computers integrated or associated with, or positioned on,the locomotives. For example, the presently-invented system or any ofits functional components can be implemented wholly or partially on atrain management computer, a Positive Train Control (PTC) computer, anon-board controller or computer, a railcar computer, and the like. Inaddition, certain aspects of the presently-invented systems and methodsmay be implemented in a laboratory environment in one or more computersor servers. Still further, the functions and computer-implementedfeatures of the present invention may be in the form of software,firmware, hardware, programmed control systems, microprocessors, and thelike.

As shown in FIG. 3, computers 900, 944, in a computing systemenvironment 902 are provided. This computing system environment 902 mayinclude, but is not limited to, at least one computer 900 having certaincomponents for appropriate operation, execution of code, and creationand communication of data. For example, the computer 900 includes aprocessing unit 904 (typically referred to as a central processing unitor CPU) that serves to execute computer-based instructions received inthe appropriate data form and format. Further, this processing unit 904may be in the form of multiple processors executing code in series, inparallel, or in any other manner for appropriate implementation of thecomputer-based instructions.

In order to facilitate appropriate data communication and processinginformation between the various components of the computer 900, a systembus 906 is utilized. The system bus 906 may be any of several types ofbus structures, including a memory bus or memory controller, aperipheral bus, or a local bus using any of a variety of busarchitectures. In particular, the system bus 906 facilitates data andinformation communication between the various components (whetherinternal or external to the computer 900) through a variety ofinterfaces, as discussed hereinafter.

The computer 900 may include a variety of discrete computer-readablemedia components. For example, this computer-readable media may includeany media that can be accessed by the computer 900, such as volatilemedia, non-volatile media, removable media, non-removable media, etc. Asa further example, this computer-readable media may include computerstorage media, such as media implemented in any method or technology forstorage of information, such as computer-readable instructions, datastructures, program modules, or other data, random access memory (RAM),read only memory (ROM), electrically erasable programmable read onlymemory (EEPROM), flash memory, or other memory technology, CD-ROM,digital versatile disks (DVDs), or other optical disk storage, magneticcassettes, magnetic tape, magnetic disk storage, or other magneticstorage devices, or any other medium which can be used to store thedesired information and which can be accessed by the computer 900.Further, this computer-readable media may include communications media,such as computer-readable instructions, data structures, programmodules, or other data in other transport mechanisms and include anyinformation delivery media, wired media (such as a wired network and adirect-wired connection), and wireless media. Computer-readable mediamay include all machine-readable media with the sole exception oftransitory, propagating signals. Of course, combinations of any of theabove should also be included within the scope of computer-readablemedia.

As seen in FIG. 3, the computer 900 further includes a system memory 908with computer storage media in the form of volatile and non-volatilememory, such as ROM and RAM. A basic input/output system (BIOS) withappropriate computer-based routines assists in transferring informationbetween components within the computer 900 and is normally stored inROM. The RAM portion of the system memory 908 typically contains dataand program modules that are immediately accessible to or presentlybeing operated on by processing unit 904, e.g., an operating system,application programming interfaces, application programs, programmodules, program data and other instruction-based computer-readablecodes.

With continued reference to FIG. 3, the computer 900 may also includeother removable or non-removable, volatile or non-volatile computerstorage media products. For example, the computer 900 may include anon-removable memory interface 910 that communicates with and controls ahard disk drive 912, i.e., a non-removable, non-volatile magneticmedium; and a removable, non-volatile memory interface 914 thatcommunicates with and controls a magnetic disk drive unit 916 (whichreads from and writes to a removable, non-volatile magnetic disk 918),an optical disk drive unit 920 (which reads from and writes to aremovable, non-volatile optical disk 922, such as a CD ROM), a UniversalSerial Bus (USB) port 921 for use in connection with a removable memorycard, etc. However, it is envisioned that other removable ornon-removable, volatile or non-volatile computer storage media can beused in the exemplary computing system environment 900, including, butnot limited to, magnetic tape cassettes, DVDs, digital video tape, solidstate RAM, solid state ROM, etc. These various removable ornon-removable, volatile or non-volatile magnetic media are incommunication with the processing unit 904 and other components of thecomputer 900 via the system bus 906. The drives and their associatedcomputer storage media discussed above and illustrated in FIG. 3 providestorage of operating systems, computer-readable instructions,application programs, data structures, program modules, program data andother instruction-based computer-readable code for the computer 900(whether duplicative or not of this information and data in the systemmemory 908).

A user may enter commands, information, and data into the computer 900through certain attachable or operable input devices, such as a keyboard924, a mouse 926, etc., via a user input interface 928. Of course, avariety of such input devices may be utilized, e.g., a microphone, atrackball, a joystick, a touchpad, a touch-screen, a scanner, etc.,including any arrangement that facilitates the input of data, andinformation to the computer 900 from an outside source. As discussed,these and other input devices are often connected to the processing unit904 through the user input interface 928 coupled to the system bus 906,but may be connected by other interface and bus structures, such as aparallel port, game port, or a universal serial bus (USB). Stillfurther, data and information can be presented or provided to a user inan intelligible form or format through certain output devices, such as amonitor 930 (to visually display this information and data in electronicform), a printer 932 (to physically display this information and data inprint form), a speaker 934 (to audibly present this information and datain audible form), etc. All of these devices are in communication withthe computer 900 through an output interface 936 coupled to the systembus 906. It is envisioned that any such peripheral output devices beused to provide information and data to the user.

The computer 900 may operate in a network environment 938 through theuse of a communications device 940, which is integral to the computer orremote therefrom. This communications device 940 is operable by and incommunication to the other components of the computer 900 through acommunications interface 942. Using such an arrangement, the computer900 may connect with or otherwise communicate with one or more remotecomputers, such as a remote computer 944, which may be a personalcomputer, a server, a router, a network personal computer, a peerdevice, or other common network nodes, and typically includes many orall of the components described above in connection with the computer900. Using appropriate communication devices 940, e.g., a modem, anetwork interface or adapter, etc., the computer 900 may operate withinand communication through a local area network (LAN) and a wide areanetwork (WAN), but may also include other networks such as a virtualprivate network (VPN), an office network, an enterprise network, anintranet, the Internet, etc. It will be appreciated that the networkconnections shown are exemplary and other means of establishing acommunications link between the computers 900, 944 may be used.

As used herein, the computer 900 includes or is operable to executeappropriate custom-designed or conventional software to perform andimplement the processing steps of the method and system of the presentinvention, thereby, forming a specialized and particular computingsystem. Accordingly, the presently-invented method and system mayinclude one or more computers 900 or similar computing devices having acomputer-readable storage medium capable of storing computer-readableprogram code or instructions that cause the processing unit 902 toexecute, configure or otherwise implement the methods, processes, andtransformational data manipulations discussed hereinafter in connectionwith the present invention. Still further, the computer 900 may be inthe form of a personal computer, a personal digital assistant, aportable computer, a laptop, a palmtop, a mobile device, a mobiletelephone, a server, or any other type of computing device having thenecessary processing hardware to appropriately process data toeffectively implement the presently-invented computer-implemented methodand system.

Although the invention has been described in detail for the purpose ofillustration based on what is currently considered to be the mostpractical and preferred embodiments, it is to be understood that suchdetail is solely for that purpose and that the invention is not limitedto the disclosed embodiments, but, on the contrary, is intended to covermodifications and equivalent arrangements that are within the spirit andscope of the appended claims. For example, it is to be understood thatthe present invention contemplates that, to the extent possible, one ormore features of any embodiment can be combined with one or morefeatures of any other embodiment.

What is claimed is:
 1. A braking system including dynamic braking for atrain having at least one locomotive with at least one on-board computerconfigured or programmed to: (a) before or during at least one brakingevent, determine predicted acceleration or deceleration of the trainbased at least partially upon an on-board braking model; (b) during theat least one braking event, determine actual train acceleration ordeceleration of the train based at least partially upon sensed,measured, and/or calculated operating conditions; and (c) adjust atleast one variable of the on-board braking model based at leastpartially on a specified difference between the predicted accelerationor deceleration and the actual acceleration or deceleration, wherein theon-board braking model is generated or modified based at least partiallyon a determined retarding force provided by each equipped or applicableaxle of the train, and wherein the at least one on-board computer isfurther configured or programmed to determine the retarding force basedat least partially on determining, sensing, and/or measuring theoperating status, performance, available force, and/or condition of atleast one of the following: (i) the at least one locomotive; (ii) atleast one locomotive consist; (iii) at least one component of a dynamicbrake system, or any combination thereof.
 2. The braking sytem of claim1, wherein at least one of steps (a)-(c) is implemented or occurssubstantially in real-time.
 3. The braking system of claim 1, furthercomprising determining the retarding force based at least partially onthe level of dynamic brake excitation and/or measured dynamic brakeenergy.
 4. The braking system of claim 1, further comprising determiningretarding force based at least partially on railroad operating rulesand/or cut-out axles.
 5. A braking system including dynamic braking fora train having at least one locomotive with at least one on-boardcomputer configured or programmed to: (a) before or during at least onebraking event, determine predicted acceleration or deceleration of thetrain based at least partially upon an on-board braking model; (b)during the at least one braking event, determine actual trainacceleration or deceleration of the train based at least partially uponsensed, measured, and/or calculated operating conditions; and (c) adjustat least one variable of the on-board braking model based at leastpartially on a specified difference between the predicted accelerationor deceleration and the actual acceleration or deceleration, wherein theon-board braking model is generated or modified based at least partiallyon a determined retarding force provided by each equipped or applicableaxle of the train, wherein dynamic brake-generated retarding force isdetermined using the following formulae:total axle count=(number of cut-in locomotives)*(axles per locomotive)if (total axle count>max DB axle count per rule) then (total axlecount=max axle count)retarding force=(total axle count−de-rated axle count)*(DB force peraxle).
 6. The braking system of claim 1, further comprising accumulatingor analyzing the predicted train acceleration or deceleration and theactual train acceleration or deceleration over a period of time, andoptionally, normalizing the accumulated data.
 7. A braking systemincluding dynamic braking for a train having at least one locomotivewith at least one on-board computer configured or programmed to: (a)before or during at least one braking event, determine predictedacceleration or deceleration of the train based at least partially uponan on-board braking model; (b) during the at least one braking event,determine actual train acceleration or deceleration of the train basedat least artially upon sensed, measured, and/or calculated operatingconditions; and (c) adjust at least one variable of the on-board brakingmodel based at least partially on a specified difference between thepredicted acceleration or deceleration and the actual acceleration ordeceleration, wherein the on-board braking model is generated ormodified based at least partially on a determined retarding forceprovided by each equipped or applicable axle of the train, wherein: (a)if the actual train deceleration is less than the predicted traindeceleration by a specified amount, the adjustment step (c) comprises:(i) removing one axle's worth of force; or (ii) de-rating one axle'sworth of force, in subsequent brake model calculations; or (b) if theactual train deceleration is greater than the predicted traindeceleration by a specified amount, the adjustment step (b) comprises atleast one of: (i) adding one axle's worth of force; or (ii) rating oneaxle's worth of force, in subsequent brake model calculations.
 8. Thebraking system of claim 7, further comprising repeating adjustment step(b) for the predicted train acceleration or deceleration and the actualtrain acceleration or deceleration over a subsequent period of time. 9.The braking system of claim 8, wherein, upon reducing the differencebetween the predicted train acceleration or deceleration and the actualtrain acceleration or deceleration to a specified level, adjusting thebraking model for subsequent braking events.
 10. The braking system ofclaim 1, wherein at least one safety factor of the on-board brakingmodel is generated by: (a) receiving or determining at least one initialsafety factor; (b) receiving or determining at least one dynamic brakingadjustment factor based at least partially on (i) the expected dynamicbraking force, and (ii) specified retarding forces of the train; and (c)determining at least one new safety factor based at least partially onthe initial safety factor and the dynamic braking adjustment factor. 11.The braking system of claim 1, wherein the at least one variablecomprises dynamic braking force data.